We consider an autonomous agent operating in a stochastic, partially-observable, multiagent environment, that explicitly models the other agents as probabilistic deterministic finite-state controllers (PDFCs) in order to predict their actions. We assume that such models are not given to the agent, but instead must be learned from (possibly imperfect) observations of the other agents' behavior. The agent maintains a belief over the other agents' models, that is updated via Bayesian inference. To represent this belief we place a flexible stick-breaking distribution over PDFCs, that allows the posterior to concentrate around controllers whose size is not bounded and scales with the complexity of the observed data. Since this Bayesian inference task is not analytically tractable, we devise a Markov chain Monte Carlo algorithm to approximate the posterior distribution. The agent then embeds the result of this inference into its own decision making process using the interactive POMDP framework. We show that our learning algorithm can learn agent models that are behaviorally accurate for problems of varying complexity, and that the agent's performance increases as a result.

When operating in stochastic, partially observable, multiagent settings, it is crucial to accurately predict the actions of other agents. In my thesis work, I propose methodologies for learning the policy of external agents from their observed behavior, in the form of finite state controllers. To perform this task, I adopt Bayesian learning algorithms based on nonparametric prior distributions, that provide the flexibility required to infer models of unknown complexity. These methods are to be embedded in decision making frameworks for autonomous planning in partially observable multiagent systems.

Museums are increasingly embracing technologies that provide highly-individualized and highly-interactive experiences to visitors. With embodied interaction experiences, increased localization accuracy supports greater nuance in interaction design, but there is usually a tradeoff between fast, accurate tracking and the ability to preserve the identity of users. Customization of experience relies on the ability to detect the identity of visitors, however. We present a method that combines fine-grained indoor tracking with robust preservation of the unique identities of multiple users. Our model merges input from an RFID reader with input from a commercial camera-based tracking system. We developed a probabilistic Bayesian model to infer at run-time the correct identification of the subjects in the camera's field of view. This method, tested in a lab and at a local museum, requires minimal modification to the exhibition space, while addressing several identity-preservation problems for which many indoor tracking systems do not have robust solutions.

Multi-FPGA systems (MFS's) represent a promising technology for various applications, such as the implementation of supercomputers and parallel and computational intensive emulation systems. On the other hand, dynamic reconfigurability expands the possibilities of traditional FPGAs by providing them the capability of adapting their functionality while still running to cope with runtime environment changes. These two research directions are merged together in this work, that describes a methodology for designing dynamic reconfigurable MFS's. In this paper a novel MFS design flow has been described, which makes use of blocks reuse through dynamic reconfigurability to make the implementation of large systems feasible even on multi-FPGA architectures with strict physical constraints. Functional to this goal is the development of an algorithm for the extraction of the isomorphic structures of a circuit that extensively exploits the hierarchy of the design.

Reconfigurable devices, such as FPGAs, introduce into the design workflow of embedded systems a new degree of freedom: the designer can have the system autonomously modify the functionality carried out by the IP-core according to the application's changing needs while it runs. The Caronte methodology, based on the modular design approach, is a design workflow that allows the creation and the handling of partial dynamic reconfigurable architectures using Xilinx FPGAs. In order to speed up its execution, it is important to succeed in quickly generate the EDK-based systems that the flow requires for the elaboration of the correct partial reconfiguration bitstreams. To achieve this goal, an IP-core generator framework has been developed, it receives as input the VHDL description of the core functionality of a module, automatically produces as output an IP-core suitable to be inserted into an EDK system. This binding can be performed in a faster way than using EDK to re-create each time the entire architecture, exploiting the EDK system creator tool. IP-core generator can be used each time an IP-core has to be created, and not only in a dynamic reconfigurability environment. Several tests are presented to validate the proposed methodology